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MHA-Net: A Multibranch Hybrid Attention Network for Medical Image Segmentation
The robust segmentation of organs from the medical image is the key technique in medical image analysis for disease diagnosis. U-Net is a robust structure for medical image segmentation. However, U-Net adopts consecutive downsampling encoders to capture multiscale features, resulting in the loss of...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9560845/ https://www.ncbi.nlm.nih.gov/pubmed/36245836 http://dx.doi.org/10.1155/2022/8375981 |
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author | Zhang, Meifang Sun, Qi Cai, Fanggang Yang, Changcai |
author_facet | Zhang, Meifang Sun, Qi Cai, Fanggang Yang, Changcai |
author_sort | Zhang, Meifang |
collection | PubMed |
description | The robust segmentation of organs from the medical image is the key technique in medical image analysis for disease diagnosis. U-Net is a robust structure for medical image segmentation. However, U-Net adopts consecutive downsampling encoders to capture multiscale features, resulting in the loss of contextual information and insufficient recovery of high-level semantic features. In this paper, we present a new multibranch hybrid attention network (MHA-Net) to capture more contextual information and high-level semantic features. The main idea of our proposed MHA-Net is to use the multibranch hybrid attention feature decoder to recover more high-level semantic features. The lightweight pyramid split attention (PSA) module is used to connect the encoder and decoder subnetwork to obtain a richer multiscale feature map. We compare the proposed MHA-Net to state-of-art approaches on the DRIVE dataset, the fluoroscopic roentgenographic stereophotogrammetric analysis X-ray dataset, and the polyp dataset. The experimental results on different modal images reveal that our proposed MHA-Net provides better segmentation results than other segmentation approaches. |
format | Online Article Text |
id | pubmed-9560845 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-95608452022-10-14 MHA-Net: A Multibranch Hybrid Attention Network for Medical Image Segmentation Zhang, Meifang Sun, Qi Cai, Fanggang Yang, Changcai Comput Math Methods Med Research Article The robust segmentation of organs from the medical image is the key technique in medical image analysis for disease diagnosis. U-Net is a robust structure for medical image segmentation. However, U-Net adopts consecutive downsampling encoders to capture multiscale features, resulting in the loss of contextual information and insufficient recovery of high-level semantic features. In this paper, we present a new multibranch hybrid attention network (MHA-Net) to capture more contextual information and high-level semantic features. The main idea of our proposed MHA-Net is to use the multibranch hybrid attention feature decoder to recover more high-level semantic features. The lightweight pyramid split attention (PSA) module is used to connect the encoder and decoder subnetwork to obtain a richer multiscale feature map. We compare the proposed MHA-Net to state-of-art approaches on the DRIVE dataset, the fluoroscopic roentgenographic stereophotogrammetric analysis X-ray dataset, and the polyp dataset. The experimental results on different modal images reveal that our proposed MHA-Net provides better segmentation results than other segmentation approaches. Hindawi 2022-10-06 /pmc/articles/PMC9560845/ /pubmed/36245836 http://dx.doi.org/10.1155/2022/8375981 Text en Copyright © 2022 Meifang Zhang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zhang, Meifang Sun, Qi Cai, Fanggang Yang, Changcai MHA-Net: A Multibranch Hybrid Attention Network for Medical Image Segmentation |
title | MHA-Net: A Multibranch Hybrid Attention Network for Medical Image Segmentation |
title_full | MHA-Net: A Multibranch Hybrid Attention Network for Medical Image Segmentation |
title_fullStr | MHA-Net: A Multibranch Hybrid Attention Network for Medical Image Segmentation |
title_full_unstemmed | MHA-Net: A Multibranch Hybrid Attention Network for Medical Image Segmentation |
title_short | MHA-Net: A Multibranch Hybrid Attention Network for Medical Image Segmentation |
title_sort | mha-net: a multibranch hybrid attention network for medical image segmentation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9560845/ https://www.ncbi.nlm.nih.gov/pubmed/36245836 http://dx.doi.org/10.1155/2022/8375981 |
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